Metabolite identification has a crucial part in the interpretation of metabolomics study results. is definitely a challenging but essential task, because unless the identity of the analyzed metabolite is known, its quantitative data cannot be related to its biochemical part. This requires further developing and optimising the available analytical techniques in order to yield a powerful metabolite identification platform. Nuclear magnetic resonance (NMR)[1,2] and mass spectrometry (MS)[3] are the methods most commonly utilized for the structural characterisation of chemical compounds. NMR offers a rapid and detailed analysis of the structure of the (un)known compound but the technique is definitely severely limited due to its relatively low level of sensitivity. MS, on the other hand, offers high level of sensitivity and specificity[4] resulting in elemental formulas.[5] However, discerning between (positional) isomers remains a challenge, even if the core structure of the molecule is known. Furthermore, in specific, fortunately rare, instances just obtaining a protonated or deprotonated molecule can be a challenge as well. In the second option case, a more targeted approach is required to elucidate the constructions of these compounds. Obviously, an elemental method is not specific enough to identify a metabolite. Its structure can be further characterised by gas-phase fragmentation reactions, e.g. collision-induced dissociation (CID). The producing fragmentation spectrum displays the structure of the precursor ion: the people of the acquired product ions and their relative abundances characterise the structure of the precursor ion and the experimental fragmentation conditions. In this way, a fingerprint emerges with a fragmentation spectral range of the molecular framework from the precursor, and, so long as it could be obtained reproducibly, it could be used to recognize ionised fragment and substances ions.[6] The separation 625115-55-1 IC50 of metabolites ahead of detection is often accomplished used liquid Mouse monoclonal to FGR chromatography (LC) or capillary electrophoresis (CE). Ionisation can be accomplished through smooth ionisation methods like mainly, e.g., electrospray ionisation (ESI). The ions generated in the ESI resource could be fragmented using CID. Regrettably, even though the CID spectra are abundant with information, it continues to be difficult to get data inside a reproducible way.[7, 8] That is because of the fact that mainly, in beam-type tools, the precursor ions internal energy 625115-55-1 IC50 is difficult to regulate. Even more reproducible fragmentation spectra could be created using ion traps,[9] which need collisional cooling from the precursor ion for effective trapping and selective (resonance) excitation. Furthermore, through the use of multistage MS (MSn) tests, 625115-55-1 IC50 ion trap tools can provide comprehensive information for the fragmentation, assisting to characterise the constructions of metabolites thereby. Despite the developing popularity of flexible ion trap tools, in-depth evaluation of MSn spectra remains difficult due to the lack of generic software tools. The challenge stems from the multidimensionality of MSn data. The majority of the MS analysis software is well suited for analysing spectra, but not for analysing one of the most important features of MSn data: the precursor-product relations between the ions observed in separate MSn spectra. The only software available at the moment which can be used to analyse and/or compare MSn spectra is Mass Frontier (HighChem, Bratislava, Slovakia).[10] This proprietary software package, being not open-source, cannot be easily integrated into our specific workflow because it is designed to work only with the propriety data format of one vendor. Furthermore, we wanted to remove spectral artefacts using the hierarchy of observed fragments. This would require software tools that can exchange data using common mass spectrometric data exchange formats such.